Project Title:Traffic Prediction Using Time Series Analysis

Objective:

To build a machine learning model that predicts future traffic conditions based on historical traffic data, helping in traffic management and planning.

Summary:

This project focuses on using time series forecasting techniques to predict traffic conditions such as vehicle count, speed, or congestion levels on roads. By analyzing historical traffic data (which can be recorded at regular intervals), the model learns to predict future traffic patterns. Time series models like ARIMA, SARIMA, or deep learning models like LSTM (Long Short-Term Memory) are commonly used to capture temporal dependencies in traffic data.

The project typically involves the following steps:

Data Collection: Gather traffic data, which may include vehicle count, speed, and weather conditions (public datasets like METR-LA or PeMS can be used).

Data Preprocessing: Handle missing data, clean outliers, and prepare time-based features (e.g., time of day, day of the week).

Model Selection: Choose a time series model (e.g., ARIMA, SARIMA, LSTM) to predict traffic conditions.

Model Evaluation: Evaluate the model's accuracy using metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), or R² score.

Key Steps:

Collect Data – Use traffic datasets with time-series data, such as vehicle count and speed at various time intervals.

Preprocess Data – Clean and prepare data, handle missing values, and engineer time-based features (hour of the day, weekday vs. weekend).

Train Model – Use time series forecasting models (ARIMA, SARIMA, LSTM, etc.) to predict future traffic conditions.

Evaluate – Test model predictions against real traffic data and assess accuracy using MAE, RMSE, or R².

Technologies Used:

Python

Pandas (for data manipulation)

Scikit-learn (for model building and evaluation)

Statsmodels (for ARIMA and SARIMA models)

Keras/TensorFlow (for deep learning models like LSTM)

Matplotlib/Seaborn (for data visualization)

Applications:

Smart city traffic management for optimizing traffic flow and reducing congestion.

Navigation apps (e.g., Google Maps, Waze) to predict real-time traffic conditions.

Urban planning for designing roads and traffic signals based on predicted patterns.

Public transportation planning to improve bus or metro scheduling.

Expected Outcomes:

A trained model that can accurately predict future traffic conditions (e.g., vehicle count, speed).

Evaluation metrics like RMSE, MAE, or R² to measure the prediction performance.

Visualizations such as time series plots of predicted vs. actual traffic conditions.